EDA2.ipynb

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        "<a href=\"https://colab.research.google.com/github/samuelbolugee/PythonDSProjects/blob/main/EDA2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "##Exploratory Data Analysis 2\n",
        "###By Samuel B. Olugunna\n",
        "\n",
        "Welcome back to the second in a series of projects on data science and machine learning! In this project, I'll try to expand further on exploratory data analytics by looking at things like skewness, kurtosis and the five-number summary.\n",
        "\n",
        "As always, I'll try to be explanatory enough, as this is also a learning process for me.\n",
        "\n",
        "Let's begin!"
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        "###Skewness and Kurtosis\n",
        "\n",
        "The most popular probability distribution observed in data is the normal distribution, defined by a symmetrical bell-shaped curve. A good example of this would be the distribution of heights or IQ scores in a typical class of students. However, under the influence of significant causes, the normal distribution can get distorted. This distortion can be observed and calculated using skewness and kurtosis.\n",
        "\n",
        "Skewness is a measure of symmetry, or more precisely, the lack of symmetry. A distribution, or data set, is symmetric if it looks the same to the left and right of the center point. Kurtosis is a measure of whether the data are heavy-tailed or light-tailed relative to a normal distribution.\n",
        "\n",
        "If a distribution is symmetric, the mean, median, and mode are all the same, and the skewness is zero. However, if a distribution is skewed, the mean, median, and mode are different, and the skewness is non-zero. Skewness can be positive, indicating that the distribution has a long tail on the right side (the values are more concentrated towards the right side, and the left tail is spread out), or negative, indicating that the distribution has a long tail on the left side (the data points are more concentrated towards the right-hand side of the distribution, making the mean, median, and mode bend towards the right). Skewness can be measured using a variety of statistics, such as the Pearson skewness coefficient or the Bowley skewness coefficient.\n",
        "\n",
        "Kurtosis, on the other hand, is a measure of the peakedness of a probability distribution. A distribution with high kurtosis has a sharper peak and heavier tails than a distribution with low kurtosis. A distribution with kurtosis equal to three is called mesokurtic and has a normal distribution. A distribution with kurtosis greater than three is called leptokurtic and has more data points clustered around the mean, while a distribution with kurtosis less than three is called platykurtic and has fewer data points clustered around the mean. Kurtosis can be measured using statistics such as the Pearson kurtosis coefficient or the Fisher kurtosis coefficient.\n",
        "\n",
        "Understanding the shape of data is crucial while practicing data science. It helps to understand where the most information lies and analyze the outliers in a given data. \n",
        "\n",
        "See below for an illustration of skewness and kurtosis:"
      ],
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        "![kurt1.png](data:image/png;base64,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) \n",
        "\n",
        "***Representation of kurtosis in probability distributions.***\n",
        "\n",
        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)\n",
        "\n",
        "\n",
        "***Representation of skewness in probability distributions.***\n"
      ],
      "metadata": {
        "id": "RsP5dl9l-22F"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "With that being said, let's demonstrate!\n",
        "\n",
        "##Using SciPy to Calculate Skewness and Kurtosis:\n",
        "\n",
        "Firstly, we must import SciPy. This will help us run the calculations for both skewness and kurtosis.\n",
        "\n",
        "Next, we create a dataset (or utilize an existing dataset).\n",
        "\n",
        "Finally, we call the scipy.stats.skew() or scipy.stats.kurtosis() function, pass in the necessary parameters and get our values.\n",
        "\n",
        "Let's take them one after the other, beginning with skewness."
      ],
      "metadata": {
        "id": "turXlhRwA7DI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import scipy\n",
        "from scipy.stats import skew"
      ],
      "metadata": {
        "id": "UbN1AN9eCA41"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating a dataset\n",
        "dataset = [8, 5, 2, 7, 6, 7.7, 7.4, 6,\n",
        "\t\t8.1, 9.5, 7.7, 8.8, 5, 3.6, 10]\n",
        "\n",
        "# Calculate the skewness\n",
        "print(skew(dataset, axis=0, bias=True))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qWAnJZbmCPNe",
        "outputId": "4a69394e-b177-4608-f351-ada915ef5e3f"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-0.606620273705261\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Looking at the scipy.stats.skew() function, we can get the following information:\n",
        "\n",
        "Syntax:\n",
        "\n",
        "```\n",
        "scipy.stats.skew(a, axis=0, bias=True, nan_policy='propagate', *, keepdims=False)\n",
        "```\n",
        "\n",
        "Parameters:\n",
        "\n",
        "a: The input array (or object) containing elements.\n",
        "\n",
        "axis: It signifies the axis along which we want to find the skewness value (By default axis = 0). If an int, the axis of the input along which to compute the statistic. The statistic of each axis-slice (e.g. row) of the input will appear in a corresponding element of the output.\n",
        "\n",
        "bias = False: Calculations are corrected to statistical bias.\n",
        "\n",
        "Computes the skewness value of the data set, along the axis.\n",
        "\n",
        "See the [scipy docs](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.skew.html) for more on this function."
      ],
      "metadata": {
        "id": "A7bmJCuACkYU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "For kurtosis, the only thing that really needs to change is what you're importing, and what you're calling (function-wise)."
      ],
      "metadata": {
        "id": "Gv1jnoLqE2Er"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.stats import kurtosis\n",
        "\n",
        "# Creating a dataset\n",
        "data = [8, 5, 2, 7, 6, 7.7, 7.4, 6,\n",
        "\t\t8.1, 9.5, 7.7, 8.8, 5, 3.6, 10]\n",
        "\n",
        "# Calculate the kurtosis\n",
        "print(kurtosis(data, axis=0, fisher = False, bias=True))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M9mBSUN1Cr0p",
        "outputId": "6c5105fb-7a5e-409d-db2a-a0370651420f"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.7253393283294978\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The scipy.stats.kurtosis() function is described as thus from the [Scipy docs:](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.kurtosis.html)\n",
        "\n",
        "**Syntax:**\n",
        "\n",
        "`scipy.stats.kurtosis(a, axis=0, fisher=True, bias=True, nan_policy='propagate', *, keepdims=False)`\n",
        "\n",
        "Computes the kurtosis (Fisher or Pearson) of a dataset.\n",
        "\n",
        "Kurtosis is the fourth central moment divided by the square of the variance. If Fisher’s definition is used, then 3.0 is subtracted from the result to give 0.0 for a normal distribution.\n",
        "\n",
        "If bias is False then the kurtosis is calculated using k statistics to eliminate bias coming from biased moment estimators.\n",
        "\n",
        "**Parameters:**\n",
        "\n",
        "a: The input array or object having the elements.\n",
        "\n",
        "axis: It represents the axis along which the kurtosis value is to be measured. By default axis = 0.\n",
        "\n",
        "fisher = True: The fisher’s definition will be used (normal 0.0).\n",
        "\n",
        "fisher =  False: The Pearson’s definition will be used (normal 3.0).\n",
        "\n",
        "Bias = True: Calculations are corrected for statistical bias, if set to False.\n",
        "\n",
        "You can check the docs for more information."
      ],
      "metadata": {
        "id": "nM5gxFnmFPTt"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let us interpret these values, shall we:\n",
        "\n",
        "Firstly, the skewness value we obtained was negative (-0.606).\n",
        "\n",
        "If skewness is less than -1 or greater than 1, the distribution is highly skewed. If skewness is between -1 and -0.5 or between 0.5 and 1, the distribution is moderately skewed. If skewness is between -0.5 and 0.5, the distribution is approximately symmetric.\n",
        "\n",
        "Since our value is negative and falls between -0.5 and -1, it is moderately left-skewed.\n",
        "\n",
        "For kurtosis, however, we used the Pearson coefficient in our computations, giving us a value of 2.72, which is less than 3, hence the data is platykurtic.\n"
      ],
      "metadata": {
        "id": "2HXYCq9gGf5s"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Visualizing the data\n",
        "\n",
        "For us to properly visualize the data, we must first fit a normal distribution to our data. This can be done by calling the norm.fit() method on our data. this will first compute the mean (mu) and standard deviation (sigma), then fit the data with these parameters. Afterwards, we then plot the histogram (very important, as this is what will our curve will be displayed upon, failure to do this will result in a \"linear curve\" situation, rather than a bell curve), and then plot the probability distribution function. This is achieved using the norm.pdf() function.\n",
        "\n",
        "The link to info on the scipy.stats.norm object can be found [here.](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.norm.html)\n",
        "\n",
        "Thanks to this [Stack Overflow](https://stackoverflow.com/questions/20011122/fitting-a-normal-distribution-to-1d-data) article for insights on how to achieve this."
      ],
      "metadata": {
        "id": "qrYjsC1bIu67"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from scipy.stats import norm\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Use our dataset\n",
        "data = data\n",
        "\n",
        "# Fit a normal distribution to the data:\n",
        "mu, std = norm.fit(data)\n",
        "\n",
        "# Plot the histogram\n",
        "plt.hist(data, bins=25, density=True, alpha=0.6, color='r')\n",
        "\n",
        "# Plotting the probability density function\n",
        "xmin, xmax = plt.xlim()\n",
        "x = np.linspace(xmin, xmax, 100)\n",
        "p = norm.pdf(x, mu, std)\n",
        "plt.plot(x, p, 'k', linewidth=2)\n",
        "title = \"Visualizing the data with results: mu (mean) = %.2f,  std = %.2f\" % (mu, std)\n",
        "plt.title(title)\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "d9tIHSsEK5TW",
        "outputId": "4efc094f-91c6-405b-90ad-fa665dc53b50"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Using Pandas to Calculate Kurtosis of data on a Series\n",
        "\n"
      ],
      "metadata": {
        "id": "L7G4KsSxO7e9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "As you may have noticed, our data so far takes the form of numbers in a list. But say you wish to perform this operation on a Pandas DataFrame. How do you go about it?\n",
        "\n",
        "Well, it's quite easy. But before we dive further into the specifics, we must take note of one thing: our data is **NOT** a Pandas DataFrame. So, we must convert our list to a Pandas DataFrame.\n",
        "\n",
        "To do this, we just pass the list into the Pandas.DataFrame() function, and voila! That's all.\n",
        "\n",
        "It will interest you to note that the Pandas DataFrame has a method called ***kurt()*** which computes the kurtosis for a set of values across a specific axis (i.e., a row or a column). By default, it computes the Fisher's kurtosis, which is basically 3 subtracted from the Pearson's kurtosis (meaning that a normal distribution has a kurtosis value - based on this method - of zero).\n",
        "\n",
        "Enough talk, let's get to it!"
      ],
      "metadata": {
        "id": "1O2qAzHrPYQQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "df = pd.DataFrame(data)\n",
        "result = df.kurt()\n",
        "\n",
        "print(result)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m22bHyzPRNET",
        "outputId": "0a851a34-028c-4f8b-ad0f-2779a6677d1c"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0    0.144077\n",
            "dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Parametric and Non-Parametric Data Summarization\n",
        "\n",
        "Data summarization helps describe the distribution of data using a few key measurements.\n",
        "\n",
        "Parametric data summarization makes use of data based on assumptions about the distribution of population from which the sample was taken. A good example would be a normal distribution, where calculating both the mean and standard deviation would give valuable insights into undesrtanding and replicating the data.\n",
        "\n",
        "In situations where the data does not follow a normal distribution, we turn to non-parametric data summarization technique. And one such example is the five-number summary.\n",
        "\n",
        "The five-number summary involves the calculation of 5 statistical quantities: namely:\n",
        "\n",
        "1. Median: The middle value in the sample, also called the 50th percentile or the 2nd quartile.\n",
        "2. 1st Quartile: The 25th percentile.\n",
        "3. 3rd Quartile: The 75th percentile.\n",
        "4. Minimum: The smallest observation in the sample.\n",
        "5. Maximum: The largest observation in the sample.\n",
        "A quartile is an observed value at a point that aids in splitting the ordered data sample into four equally sized parts. The median, or 2nd Quartile, splits the ordered data sample into two parts, and the 1st and 3rd quartiles split each of those halves into quarters. Percentiles split the ordered data into 100 equal parts.\n",
        "\n",
        "With that being said, let's see how to achieve this.\n",
        "\n",
        "There is a NumPy function called the percentile() function that helps with calculating percentiles. It takes the data as an argument, and a list of percentiles to calculate. It returns a list of values corresponding to the provided percentile values, and these values can be accessed via simple list indexing. \n",
        "\n",
        "Here is what I mean:\n",
        "\n",
        "```\n",
        "numpy.percentile(dataset, [25,50,75])\n",
        "```\n",
        "\n",
        "The data and list of percentiles to calculate are already provided.\n",
        "Finding the highest and lowest valyes involves calling the min() and max() functions on the data. Simple."
      ],
      "metadata": {
        "id": "_vrHB4DuTJ8n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from numpy import percentile\n",
        "\n",
        "# call our dataset\n",
        "data = data\n",
        "# calculate the 1st, 2nd and 3rd quartiles\n",
        "quartiles = percentile(data, [25, 50, 75])\n",
        "# Find highest and lowest values: remember that in this case we must pass the list as an argument into the functions.\n",
        "data_min, data_max = min(data), max(data)\n",
        "# print 5-number summary, to 3 decimal places.\n",
        "print('Min: %.3f' % data_min)\n",
        "print('Q1: %.3f' % quartiles[0])\n",
        "print('Median: %.3f' % quartiles[1])\n",
        "print('Q3: %.3f' % quartiles[2])\n",
        "print('Max: %.3f' % data_max)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hnrz31BWWHCX",
        "outputId": "47cd76cf-2190-47f9-b0d5-117c7d269acc"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Min: 2.000\n",
            "Q1: 5.500\n",
            "Median: 7.400\n",
            "Q3: 8.050\n",
            "Max: 10.000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "That's it for now! In the final EDA notebook, I will put all of this and more into practice with a dataset to try and gain relevant insights into the underlying data. Thanks!"
      ],
      "metadata": {
        "id": "SwUc78U3cT2K"
      }
    }
  ]
}